def ln_linear(inputs, size, bias, concat=False, dtype=None, scope=None):
if not isinstance(size, (list, tuple)):
raise ValueError("size argument must be (input_size, output_size)")
input_size, output_size = size
if not isinstance(input_size, (list, tuple)):
input_size = [input_size]
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
if len(inputs) != len(input_size):
raise RuntimeError("unmatched elements found: inputs and input_size")
results = []
with variable_scope(scope):
if concat:
input_size = sum(input_size)
inputs = theano.tensor.concatenate(inputs, -1)
shape = [input_size, output_size]
matrix = get_variable("matrix", shape, dtype=dtype)
res = theano.dot(inputs, matrix)
with variable_scope("layer_norm"):
alpha = get_variable("gains", shape=(output_size,), dtype=dtype, initializer=ones_initializer)
beta = get_variable("biases", shape=(output_size,), dtype=dtype, initializer=zeros_initializer)
res = layer_normalize(res, alpha, beta)
results.append(res)
else:
for i in range(len(input_size)):
shape = [input_size[i], output_size]
name = "matrix_%d" % i
matrix = get_variable(name, shape, dtype=dtype)
res = theano.dot(inputs[i], matrix)
with variable_scope("layer_norm"):
alpha = get_variable("gains_%d" % i, shape=(output_size,), dtype=dtype,
initializer=ones_initializer())
beta = get_variable("biases_%d" % i, shape=(output_size,), dtype=dtype,
initializer=zeros_initializer())
res = layer_normalize(res, alpha, beta)
results.append(res)
if bias:
shape = [output_size]
bias = get_variable("bias", shape, dtype=dtype)
results.append(bias)
if len(results) == 1:
return results[0]
return reduce(theano.tensor.add, results)
def encoder(cell, inputs, mask, initial_state=None, dtype=None, scope=None):
with ops.variable_scope(scope or "encoder"):
with ops.variable_scope("forward"):
fd_states = gru_encoder(cell, inputs, mask, initial_state, dtype)
with ops.variable_scope("backward"):
inputs = inputs[::-1]
mask = mask[::-1]
bd_states = gru_encoder(cell, inputs, mask, initial_state, dtype)
bd_states = bd_states[::-1]
return fd_states, bd_states
def decoder(cell, inputs, mask, initial_state, attention_states,
attention_mask, attn_size, dtype=None, scope=None):
input_size, states_size = cell.input_size
output_size = cell.output_size
dtype = dtype or inputs.dtype
# non sequences should passed to scan, DO NOT use closure
def loop_fn(inputs, mask, state, attn_states, attn_mask, m_states):
mask = mask[:, None]
alpha = attention(state, m_states, output_size, attn_size, attn_mask)
context = theano.tensor.sum(alpha[:, :, None] * attn_states, 0)
output, next_state = cell([inputs, context], state)
next_state = (1.0 - mask) * state + mask * next_state
return [next_state, context]
with ops.variable_scope(scope or "decoder"):
mapped_states = map_attention_states(attention_states, states_size,
attn_size)
seq = [inputs, mask]
outputs_info = [initial_state, None]
non_seq = [attention_states, attention_mask, mapped_states]
(states, contexts) = ops.scan(loop_fn, seq, outputs_info, non_seq)
return states, contexts
def __call__(self, inputs, state, scope=None):
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
input_size = self.input_size
output_size = self.output_size
if len(inputs) != len(input_size):
raise RuntimeError("unmatched elements: inputs and input_size")
size = [list(input_size) + [output_size], 4 * output_size]
with variable_scope(scope or "lstm"):
c, h = state
new_inputs = list(inputs[:]) + [h]
concat = linear(new_inputs, size, True, concat=True, scope="gates")
i, j, f, o = theano.tensor.split(concat, [output_size] * 4, 4, -1)
j = theano.tensor.tanh(j)
# input, forget, output gate
i = theano.tensor.nnet.sigmoid(i)
f = theano.tensor.nnet.sigmoid(f)
o = theano.tensor.nnet.sigmoid(o)
new_c = c * f + i * j
# no output activation
new_h = new_c * o
new_state = (new_c, new_h)
return new_h, new_state
def attention(query,
mapped_states,
state_size,
attn_size,
attention_mask=None,
scope=None):
with ops.variable_scope(scope or "attention"):
mapped_query = nn.linear(query, [state_size, attn_size],
False,
scope="query_w")
mapped_query = mapped_query[None, :, :]
hidden = theano.tensor.tanh(mapped_query + mapped_states)
score = nn.linear(hidden, [attn_size, 1], False, scope="attention_v")
score = score.reshape([score.shape[0], score.shape[1]])
exp_score = theano.tensor.exp(score)
if attention_mask is not None:
exp_score = exp_score * attention_mask
alpha = exp_score / theano.tensor.sum(exp_score, 0)
return alpha
def build_attention(self, src_seq, src_mask, target_inputs, tgt_seq,
tgt_mask, keys, values, initial_state):
# attention graph, this feature is optional
def attention_loop(inputs, mask, state, keys, values, key_mask):
mask = mask[:, None]
alpha = attention(state, keys, key_mask, self.dim_hid,
self.dim_key)
context = T.sum(alpha[:, :, None] * values, 0)
output, next_state = self.cell([inputs, context], state)
next_state = (1.0 - mask) * state + mask * next_state
return [alpha, next_state]
with ops.variable_scope("decoder"):
seq = [target_inputs, tgt_mask]
outputs_info = [None, initial_state]
nonseq = [keys, values, src_mask]
(alpha, state), updaptes = theano.scan(attention_loop, seq,
outputs_info, nonseq)
attention_score = alpha
alignment_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
alignment_outputs = attention_score
align = theano.function(alignment_inputs, alignment_outputs)
return align
def attention(query,
keys,
key_mask,
dim_query,
dim_key,
dtype=None,
scope=None):
with ops.variable_scope(scope or "attention", dtype=dtype):
# content-based addressing
# e_i = v_a^T tanh(W query + key_i)
# alpha = softmax({e_i})
# (n_query, dim_query) -> (n_query, dim_key)
mapped_query = nn.linear(query, [dim_query, dim_key],
False,
scope="map-query")
# (n_key, n_query, dim_key)
act = T.tanh(mapped_query[None, :, :] + keys)
# (n_key, n_query, 1)
e = nn.linear(act, [dim_key, 1], False,
scope="pre-alpha") # (n_key, n_query, 1)
# (n_key, n_query)
e = T.reshape(e, e.shape[:2])
e = e.T # (n_query, n_key)
# match dimension
key_mask = key_mask.T
alpha = nn.masked_softmax(e, key_mask) # (n_query, n_key)
alpha = alpha.T # (n_key, n_query)
return alpha
def map_attention_states(attention_states, input_size, attn_size, scope=None):
with ops.variable_scope(scope or "attention"):
mapped_states = nn.linear(attention_states, [input_size, attn_size],
False,
scope="attention_w")
return mapped_states
def __call__(self, inputs, state, scope=None):
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
input_size = self.input_size
output_size = self.output_size
if len(inputs) != len(input_size):
raise RuntimeError("unmatched elements: inputs and input_size")
size = [list(input_size) + [output_size], output_size]
with variable_scope(scope or "gru_cell"):
new_inputs = list(inputs[:]) + [state]
r = feedforward(new_inputs, size, False, scope="reset_gate")
u = feedforward(new_inputs, size, False, scope="update_gate")
new_inputs = list(inputs[:]) + [r * state]
c = feedforward(new_inputs,
size,
True,
activation=theano.tensor.tanh,
scope="candidate")
new_state = (1.0 - u) * state + u * c
return new_state, new_state
def coarseattention(query,
keys,
key_mask,
dim_query,
dim_key,
dtype=None,
scope=None):
with ops.variable_scope(scope or "coarseattention", dtype=dtype):
# content-based addressing
# e_i = v_a^T tanh(W query + key_i)
# alpha = softmax({e_i})
# (n_query, dim_query) -> (n_query, dim_key)
e = []
for i in range(len(keys)):
mapped_query = nn.linear(query, [dim_query, dim_key[i]],
False,
scope="map-query_{}".format(i))
# (n_key, n_query, dim_key)
act = T.tanh(mapped_query[None, :, :] + keys[i])
# (n_key, n_query, 1)
em = nn.linear(
act, [dim_key[i], 1], False,
scope="pre-alpha_{}".format(i)) # (n_key, n_query, 1)
e.append(em)
e = reduce(T.add, e)
# (n_key, n_query)
e = T.reshape(e, e.shape[:2])
e = e.T # (n_query, n_key)
# match dimension
key_mask = key_mask.T
alpha = nn.masked_softmax(e, key_mask) # (n_query, n_key)
alpha = alpha.T # (n_key, n_query)
return alpha
def build_sampling(self, src_seq, src_mask, target_embedding, target_bias, keys, values, initial_state):
# sampling graph, this feature is optional
max_len = T.iscalar()
def sampling_loop(inputs, state, keys, values, key_mask):
_, state_prime = self.cell1(inputs, state, scope="gru1")
alpha = attention(state_prime, keys, key_mask, self.dim_hid, self.dim_key)
context = T.sum(alpha[:, :, None] * values, 0)
output, next_state = self.cell2(context, state_prime, scope="gru2")
probs = self.prediction(inputs, next_state, context) # p(y_j) \propto f(y_{j-1}, c_j, s_j)
next_words = ops.random.multinomial(probs).argmax(axis=1)
new_inputs = nn.embedding_lookup(target_embedding, next_words)
new_inputs = new_inputs + target_bias
return [next_words, new_inputs, next_state]
with ops.variable_scope("decoder"):
batch = src_seq.shape[1]
initial_inputs = T.zeros([batch, self.dim_y], theano.config.floatX)
outputs_info = [None, initial_inputs, initial_state]
nonseq = [keys, values, src_mask]
outputs, updates = theano.scan(sampling_loop, [], outputs_info,
nonseq, n_steps=max_len)
sampled_words = outputs[0]
sampling_inputs = [src_seq, src_mask, max_len]
sampling_outputs = sampled_words
sample = theano.function(sampling_inputs, sampling_outputs,
updates=updates)
return sample
def build_attention(self, src_seq, src_mask, target_inputs, tgt_seq, tgt_mask, keys, values, initial_state):
# attention graph, this feature is optional
def attention_loop(inputs, mask, state, keys, values, key_mask):
mask = mask[:, None]
# s_j^{\prime} = GRU^1(y_{j-1}, s_{j-1})
_, state_prime = self.cell1(inputs, state, scope="gru1")
# c_j = att(H, s_j^{\prime})
alpha = attention(state_prime, keys, key_mask, self.dim_hid, self.dim_key)
context = T.sum(alpha[:, :, None] * values, 0)
# s_j = GRU^2(c_j, s_j^{\prime})
output, next_state = self.cell2(context, state_prime, scope="gru2")
next_state = (1.0 - mask) * state + mask * next_state
return [alpha, next_state]
with ops.variable_scope("decoder"):
seq = [target_inputs, tgt_mask]
outputs_info = [None, initial_state]
nonseq = [keys, values, src_mask]
(alpha, state), updaptes = theano.scan(attention_loop, seq,
outputs_info, nonseq)
attention_score = alpha
alignment_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
alignment_outputs = attention_score
align = theano.function(alignment_inputs, alignment_outputs)
return align
def decoder(cell,
inputs,
mask,
initial_state,
attention_states,
attention_mask,
attn_size,
mapped_states=None,
dtype=None,
scope=None):
input_size, states_size = cell.input_size
output_size = cell.output_size
dtype = dtype or inputs.dtype
att_size = [output_size, states_size, attn_size]
def loop_fn(inputs, mask, state, attn_states, attn_mask, mapped_states):
mask = mask[:, None]
alpha = attention(state, None, mapped_states, attn_mask, att_size)
context = theano.tensor.sum(alpha[:, :, None] * attn_states, 0)
output, next_state = cell([inputs, context], state)
next_state = (1.0 - mask) * state + mask * next_state
return [next_state, context]
with ops.variable_scope(scope or "decoder"):
if mapped_states is None:
mapped_states = attention(None, attention_states, None, None,
att_size)
seq = [inputs, mask]
outputs_info = [initial_state, None]
non_seq = [attention_states, attention_mask, mapped_states]
(states, contexts) = ops.scan(loop_fn, seq, outputs_info, non_seq)
return states, contexts
def prediction(self, y_emb, state, context, keep_prob=1.0):
"""
readout -> softmax
p(y_j) \propto f(y_{j-1}, s_{j}, c_{j})
:param y_emb:
:param state:
:param context:
:param keep_prob:
:return:
"""
features = [state, y_emb, context]
readout = nn.feedforward(
features,
[[self.dim_hid, self.dim_y, self.dim_value], self.dim_readout],
True,
activation=T.tanh,
scope="readout")
if keep_prob < 1.0:
readout = nn.dropout(readout, keep_prob=keep_prob)
with ops.variable_scope(self.tiescope, reuse=True):
target_embedding = ops.get_variable(
"embedding", [self.n_y_vocab, self.dim_readout])
target_embedding = target_embedding.T
logits = T.dot(readout, target_embedding)
# logits = nn.linear(readout, [self.dim_readout, self.n_y_vocab], True,
# scope="logits")
if logits.ndim == 3:
new_shape = [logits.shape[0] * logits.shape[1], -1]
logits = logits.reshape(new_shape)
probs = T.nnet.softmax(logits)
return probs
def forward(self,
y_seq,
y_emb,
mask,
keys,
key_mask,
values,
initial_state,
domain_keys,
domain_annot,
tag_seq,
keep_prob=1.0):
# shift embedding
y_shifted = T.zeros_like(y_emb)
y_shifted = T.set_subtensor(y_shifted[1:], y_emb[:-1])
y_emb = y_shifted
# feed
states, contexts = Decoder.scan(self, y_emb, mask, keys, key_mask,
values, initial_state, domain_keys,
domain_annot)
with ops.variable_scope("DSAdec"):
newmask = T.set_subtensor(
mask[T.cast(T.sum(mask, 0) - 1, 'int32'),
T.arange(mask.shape[1])], 0.0)
# domain_alpha = domain_sensitive_attention(states, newmask, self.dim_hid, self.dim_domain)
domain_alpha = attention(states[-1], states, newmask, self.dim_hid,
self.dim_hid)
domain_states = states * domain_alpha[:, :, None]
# batch * (shdim * 2)
domain_context = T.sum(domain_states, 0)
# batch * feadim1
feature = nn.feedforward(domain_context,
[self.dim_hid, self.feadim],
True,
activation=T.tanh,
scope="feature")
dscores = nn.feedforward(feature, [self.feadim, self.dnum],
True,
activation=T.tanh,
scope="score")
# (batch, 4)
dprobs = T.nnet.softmax(dscores)
pred_tag = T.argmax(dprobs, 1)
didx = T.arange(tag_seq.flatten().shape[0])
dce = -T.log(dprobs[didx, tag_seq.flatten()])
domaincost = T.mean(dce)
# p(y_j) \propto f(y_{j-1}, s_{j}, c_{j})
probs = self.prediction(y_emb, states, contexts, keep_prob)
# compute cost
cost, snt_cost = self.get_cost(y_seq, mask, probs, domain_alpha)
return states, contexts, cost, domaincost, pred_tag, snt_cost
def __call__(self, inputs, state, c_inputs=None, scope=None):
with variable_scope(scope or "multi_rnn_cell"):
cur_inp = inputs
new_states = []
for i, cell in enumerate(self._cells):
with variable_scope("cell_%d" % i):
cur_state = state[i]
if c_inputs:
if not isinstance(inputs, (list, tuple)):
cur_inp = [inputs]
if not isinstance(c_inputs, (list, tuple)):
c_inputs = [c_inputs]
cur_inp = list(cur_inp) + list(c_inputs)
cur_inp, new_state = cell(cur_inp, cur_state)
new_states.append(new_state)
new_states = tuple(new_states)
return cur_inp, new_states
def forward(self,
x_embedded,
mask,
initial_state=None,
dtype=None,
scope=None):
scope = scope or "encoder"
cell = self.cell
with ops.variable_scope(scope, dtype=dtype):
with ops.variable_scope("forward"):
fd_states = gru_encoder(cell, x_embedded, mask, initial_state,
dtype)
with ops.variable_scope("backward"):
x_embedded = x_embedded[::-1]
mask = mask[::-1]
bd_states = gru_encoder(cell, x_embedded, mask, initial_state,
dtype)
bd_states = bd_states[::-1]
return fd_states, bd_states
def domain_sensitive_attention(keys, key_mask, dim_key, dim_domain, dtype=None, scope=None):
with ops.variable_scope(scope or "domain_sensitive_attention", dtype=dtype):
mapped_keys = nn.linear(keys, [dim_key, dim_domain], True, scope="map-key")
act = T.tanh(mapped_keys)
# (n_key, n_query, 1)
e = nn.linear(act, [dim_domain, 1], False, scope="pre-alpha") # (n_key, n_query, 1)
# (n_key, n_query)
e = T.reshape(e, e.shape[:2])
e = e.T # (n_query, n_key)
# match dimension
key_mask = key_mask.T
alpha = nn.masked_softmax(e, key_mask) # (n_query, n_key)
alpha = alpha.T # (n_key, n_query)
return alpha
def step(self, y_prev, mask, state, *args):
n_src = self.n_src
assert len(args) == self.n_src * 3
src_keys = args[:n_src]
src_values = args[n_src:2 * n_src]
src_masks = args[2 * n_src:]
mask = mask[:, None]
# s_j^{\prime} = GRU^1(y_{j-1}, s_{j-1})
_, state_prime = self.cell1(y_prev, state, scope="gru1")
state_prime = (1.0 - mask) * state + mask * state_prime
# c_j = att(H, s_j^{\prime})
contexts = []
for i, _key, _val, _mask in zip(itertools.count(), src_keys,
src_values, src_masks):
alpha = attention(state_prime,
_key,
_mask,
self.dim_hid,
self.dim_key,
scope='attn_alpha_%d' % i)
context = theano.tensor.sum(alpha[:, :, None] * _val, 0)
contexts.append(context)
if self.method == "attn":
contexts = T.reshape(T.concatenate(contexts, 0),
[n_src] + list(contexts[0].shape))
with ops.variable_scope("beta"):
beta_keys = map_key(contexts, self.dim_value, self.dim_key)
beta = attention(state_prime,
beta_keys,
T.ones(contexts.shape[:2]),
self.dim_hid,
self.dim_key,
scope='beta')
context = T.sum(beta[:, :, None] * contexts, 0)
elif self.method == "concat":
context = T.concatenate(contexts, -1)
# s_j = GRU^2(c_j, s_j^{\prime})
output, next_state = self.cell2(context, state_prime, scope="gru2")
next_state = (1.0 - mask) * state + mask * next_state
return next_state, context
def linear(inputs, size, bias, concat=False, dtype=None, scope=None):
if not isinstance(size, (list, tuple)):
raise ValueError("size argument must be (input_size, output_size)")
input_size, output_size = size
if not isinstance(input_size, (list, tuple)):
input_size = [input_size]
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
if len(inputs) != len(input_size):
raise RuntimeError("unmatched elements found: inputs and input_size")
results = []
with variable_scope(scope):
if concat:
input_size = sum(input_size)
inputs = theano.tensor.concatenate(inputs, -1)
shape = [input_size, output_size]
matrix = get_variable("matrix", shape, dtype=dtype)
results.append(theano.dot(inputs, matrix))
else:
for i in range(len(input_size)):
shape = [input_size[i], output_size]
name = "matrix_%d" % i
matrix = get_variable(name, shape, dtype=dtype)
results.append(theano.dot(inputs[i], matrix))
if bias:
shape = [output_size]
bias = get_variable("bias", shape, dtype=dtype)
results.append(bias)
if len(results) == 1:
return results[0]
return reduce(theano.tensor.add, results)
def step(self, y_prev, mask, state, src_words_keys, src_pos_keys, pos_words_keys, pos_pos_keys, values, key_mask):
mask = mask[:, None]
# s_j^{\prime} = GRU^1(y_{j-1}, s_{j-1})
_, state_prime = self.cell1(y_prev, state, scope="gru1")
state_prime = (1.0 - mask) * state + mask * state_prime
# c_j = att(H, s_j^{\prime})
alpha = coarseattention(state_prime, [src_words_keys, src_pos_keys], key_mask, self.dim_query, [self.dim_key, self.dim_word2pos])
context = T.sum(alpha[:, :, None] * values, 0)
# s_j = GRU^2(c_j, s_j^{\prime})
output, next_state = self.cell2(context, state_prime, scope="gru2")
next_state = (1.0 - mask) * state + mask * next_state
# y_pos_{j} = nn(s_{j})
tempstate = nn.feedforward(next_state,
[self.dim_hid, self.posnndim],
True, activation=T.nnet.relu, scope="ttaggerstates")
score = nn.linear(tempstate, [self.posnndim, self.n_y_tagvocab], True, scope="ttaggerscores")
prob = T.nnet.softmax(score)
with ops.variable_scope("tgttag_embedding"):
tgttag_embedding = ops.get_variable("embedding", [self.n_y_tagvocab, self.poshdim])
tgttag_bias = ops.get_variable("bias", [self.poshdim])
y_pos_state = T.dot(prob, tgttag_embedding) + tgttag_bias
posquery = T.concatenate([tempstate, y_pos_state], -1)
beta = fineattention([state_prime, posquery], [[src_words_keys, src_pos_keys], [pos_words_keys, pos_pos_keys]], key_mask,
[self.dim_query, self.dim_posquery], [[self.dim_key, self.dim_word2pos], [self.dim_pos2word, self.dim_pos2pos]])
finalalpha = beta
# adl = ops.get_variable("adaptive", [])
# walpha = T.exp(adl)
# wbeta = T.exp(1.0 - adl)
# finalalpha = walpha/(walpha+wbeta) * alpha + wbeta/(walpha+wbeta) * beta
#
# finalalpha = 0.5 * beta + 0.5 * alpha
context = T.sum(finalalpha[:, :, None] * values, 0)
return next_state, context, y_pos_state, prob
def __init__(self, **option):
# source and target embedding dim
sedim, tedim = option["embdim"]
# source, target and attention hidden dim
shdim, thdim, ahdim = option["hidden"]
# maxout hidden dim
maxdim = option["maxhid"]
# maxout part
maxpart = option["maxpart"]
# deepout hidden dim
deephid = option["deephid"]
svocab, tvocab = option["vocabulary"]
sw2id, sid2w = svocab
tw2id, tid2w = tvocab
# source and target vocabulary size
svsize, tvsize = len(sid2w), len(tid2w)
if "scope" not in option or option["scope"] is None:
option["scope"] = "rnnsearch"
if "initializer" not in option:
option["initializer"] = None
if "regularizer" not in option:
option["regularizer"] = None
if "keep_prob" not in option:
option["keep_prob"] = 1.0
dtype = theano.config.floatX
initializer = option["initializer"]
regularizer = option["regularizer"]
keep_prob = option["keep_prob"] or 1.0
scope = option["scope"]
decoder_scope = "decoder"
encoder = Encoder(sedim, shdim)
decoderType = eval("Decoder{}".format(option["decoder"]))
decoder = decoderType(tedim, thdim, ahdim, 2 * shdim, dim_maxout=maxdim, max_part=maxpart, dim_readout=deephid,
n_y_vocab=tvsize)
# training graph
with ops.variable_scope(scope, initializer=initializer,
regularizer=regularizer, dtype=dtype):
src_seq = T.imatrix("source_sequence")
src_mask = T.matrix("source_sequence_mask")
tgt_seq = T.imatrix("target_sequence")
tgt_mask = T.matrix("target_sequence_mask")
with ops.variable_scope("source_embedding"):
source_embedding = ops.get_variable("embedding",
[svsize, sedim])
source_bias = ops.get_variable("bias", [sedim])
with ops.variable_scope("target_embedding") as tgtembscope:
target_embedding = ops.get_variable("embedding",
[tvsize, tedim])
# target_bias = ops.get_variable("bias", [tedim])
decoder.tiescope = tgtembscope
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
source_inputs = source_inputs + source_bias
if keep_prob < 1.0:
source_inputs = nn.dropout(source_inputs, keep_prob=keep_prob)
target_inputs = nn.dropout(target_inputs, keep_prob=keep_prob)
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
# compute initial state for decoder
# first state of backward encoder
final_state = r_states[0]
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True, scope="initial",
activation=T.tanh)
# keys for query
mapped_keys = map_key(annotation, 2 * shdim, ahdim)
_, _, cost,_ = decoder.forward(tgt_seq, target_inputs, tgt_mask, mapped_keys, src_mask,
annotation, initial_state, keep_prob)
training_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
training_outputs = [cost]
# decoding graph
with ops.variable_scope(scope, reuse=True):
prev_words = T.ivector("prev_words")
# disable dropout
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
source_inputs = source_inputs + source_bias
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
# target_inputs = target_inputs + target_bias
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
# decoder
final_state = r_states[0]
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True, scope="initial",
activation=T.tanh)
mapped_keys = map_key(annotation, 2 * shdim, ahdim)
prev_inputs = nn.embedding_lookup(target_embedding, prev_words)
# prev_inputs = prev_inputs + target_bias
cond = T.neq(prev_words, 0)
# zeros out embedding if y is 0, which indicates <s>
prev_inputs = prev_inputs * cond[:, None]
with ops.variable_scope(decoder_scope):
mask = T.ones_like(prev_words, dtype=dtype)
next_state, context = decoder.step(prev_inputs, mask, initial_state, mapped_keys, annotation, src_mask)
if option["decoder"] == "GruSimple":
probs = decoder.prediction(prev_inputs, initial_state, context)
elif option["decoder"] == "GruCond":
probs = decoder.prediction(prev_inputs, next_state, context)
# encoding
encoding_inputs = [src_seq, src_mask]
encoding_outputs = [annotation, initial_state, mapped_keys]
encode = theano.function(encoding_inputs, encoding_outputs)
if option["decoder"] == "GruSimple":
prediction_inputs = [prev_words, initial_state, annotation,
mapped_keys, src_mask]
prediction_outputs = [probs, context]
predict = theano.function(prediction_inputs, prediction_outputs)
generation_inputs = [prev_words, initial_state, context]
generation_outputs = next_state
generate = theano.function(generation_inputs, generation_outputs)
self.predict = predict
self.generate = generate
elif option["decoder"] == "GruCond":
prediction_inputs = [prev_words, initial_state, annotation,
mapped_keys, src_mask]
prediction_outputs = [probs, next_state]
predict = theano.function(prediction_inputs, prediction_outputs)
self.predict = predict
# optional graph
'''
with ops.variable_scope(scope, reuse=True):
sample = decoder.build_sampling(src_seq, src_mask, target_embedding, target_bias, mapped_keys,
annotation, initial_state)
align = decoder.build_attention(src_seq, src_mask, target_inputs, tgt_seq, tgt_mask, mapped_keys,
annotation, initial_state)
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True, scope="initial",
activation=T.tanh)
# keys for query
mapped_keys = map_key(annotation, 2 * shdim, ahdim)
_, _, _,snt_cost = decoder.forward(tgt_seq, target_inputs, tgt_mask, mapped_keys, src_mask,
annotation, initial_state, 1.0)
get_snt_cost = theano.function(training_inputs, snt_cost)
'''
self.cost = cost
self.inputs = training_inputs
self.outputs = training_outputs
self.updates = []
# self.align = align
# self.sample = sample
self.encode = encode
# self.get_snt_cost = get_snt_cost
self.option = option
def __init__(self, **option):
# source and target embedding dim
sedim, tedim = option["embdim"]
# source, target and attention hidden dim
shdim, thdim, ahdim = option["hidden"]
# maxout hidden dim
maxdim = option["maxhid"]
# maxout part
maxpart = option["maxpart"]
# deepout hidden dim
deephid = option["deephid"]
svocab, tvocab = option["vocabulary"]
sw2id, sid2w = svocab
tw2id, tid2w = tvocab
# source and target vocabulary size
svsize, tvsize = len(sid2w), len(tid2w)
if "scope" not in option or option["scope"] is None:
option["scope"] = "rnnsearch"
if "initializer" not in option:
option["initializer"] = None
if "regularizer" not in option:
option["regularizer"] = None
if "keep_prob" not in option:
option["keep_prob"] = 1.0
dtype = theano.config.floatX
scope = option["scope"]
initializer = option["initializer"]
regularizer = option["regularizer"]
keep_prob = option["keep_prob"] or 1.0
def prediction(prev_inputs, prev_state, context, keep_prob=1.0):
features = [prev_state, prev_inputs, context]
maxhid = nn.maxout(features, [[thdim, tedim, 2 * shdim], maxdim],
maxpart, True)
readout = nn.linear(maxhid, [maxdim, deephid], False,
scope="deepout")
if keep_prob < 1.0:
readout = nn.dropout(readout, keep_prob=keep_prob)
logits = nn.linear(readout, [deephid, tvsize], True,
scope="logits")
if logits.ndim == 3:
new_shape = [logits.shape[0] * logits.shape[1], -1]
logits = logits.reshape(new_shape)
probs = theano.tensor.nnet.softmax(logits)
return probs
# training graph
with ops.variable_scope(scope, initializer=initializer,
regularizer=regularizer, dtype=dtype):
src_seq = theano.tensor.imatrix("soruce_sequence")
src_mask = theano.tensor.matrix("soruce_sequence_mask")
tgt_seq = theano.tensor.imatrix("target_sequence")
tgt_mask = theano.tensor.matrix("target_sequence_mask")
with ops.variable_scope("source_embedding"):
source_embedding = ops.get_variable("embedding",
[svsize, sedim])
source_bias = ops.get_variable("bias", [sedim])
with ops.variable_scope("target_embedding"):
target_embedding = ops.get_variable("embedding",
[tvsize, tedim])
target_bias = ops.get_variable("bias", [tedim])
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
source_inputs = source_inputs + source_bias
target_inputs = target_inputs + target_bias
if keep_prob < 1.0:
source_inputs = nn.dropout(source_inputs, keep_prob=keep_prob)
target_inputs = nn.dropout(target_inputs, keep_prob=keep_prob)
cell = nn.rnn_cell.gru_cell([sedim, shdim])
if keep_prob < 1.0:
cell = nn.rnn_cell.dropout_wrapper(cell)
outputs = encoder(cell, source_inputs, src_mask)
annotation = theano.tensor.concatenate(outputs, 2)
# compute initial state for decoder
# first state of backward encoder
final_state = outputs[1][0]
with ops.variable_scope("decoder"):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True, scope="initial",
activation=theano.tensor.tanh)
cell = nn.rnn_cell.gru_cell([[tedim, 2 * shdim], thdim])
if keep_prob < 1.0:
cell = nn.rnn_cell.dropout_wrapper(cell)
# run decoder
decoder_outputs = decoder(cell, target_inputs, tgt_mask,
initial_state, annotation, src_mask,
ahdim)
all_output, all_context = decoder_outputs
shift_inputs = theano.tensor.zeros_like(target_inputs)
shift_inputs = theano.tensor.set_subtensor(shift_inputs[1:],
target_inputs[:-1])
init_state = initial_state[None, :, :]
all_states = theano.tensor.concatenate([init_state, all_output], 0)
prev_states = all_states[:-1]
with ops.variable_scope("decoder"):
probs = prediction(shift_inputs, prev_states, all_context,
keep_prob=keep_prob)
# compute cost
idx = theano.tensor.arange(tgt_seq.flatten().shape[0])
cost = -theano.tensor.log(probs[idx, tgt_seq.flatten()])
cost = cost.reshape(tgt_seq.shape)
cost = theano.tensor.sum(cost * tgt_mask, 0)
cost = theano.tensor.mean(cost)
training_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
training_outputs = [cost]
# decoding graph
with ops.variable_scope(scope, reuse=True):
prev_words = theano.tensor.ivector("prev_words")
# encoder, disable dropout
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
source_inputs = source_inputs + source_bias
cell = nn.rnn_cell.gru_cell([sedim, shdim])
outputs = encoder(cell, source_inputs, src_mask)
annotation = theano.tensor.concatenate(outputs, 2)
# decoder
final_state = outputs[1][0]
with ops.variable_scope("decoder"):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True, scope="initial",
activation=theano.tensor.tanh)
inputs = nn.embedding_lookup(target_embedding, prev_words)
inputs = inputs + target_bias
cond = theano.tensor.neq(prev_words, 0)
# zeros out embedding if y is 0
inputs = inputs * cond[:, None]
cell = nn.rnn_cell.gru_cell([[tedim, 2 * shdim], thdim])
with ops.variable_scope("decoder"):
mapped_states = map_attention_states(annotation, 2 * shdim,
ahdim)
alpha = attention(initial_state, mapped_states, thdim, ahdim,
src_mask)
context = theano.tensor.sum(alpha[:, :, None] * annotation, 0)
output, next_state = cell([inputs, context], initial_state)
probs = prediction(inputs, initial_state, context)
# encoding
encoding_inputs = [src_seq, src_mask]
encoding_outputs = [annotation, initial_state, mapped_states]
encode = theano.function(encoding_inputs, encoding_outputs)
prediction_inputs = [prev_words, initial_state, annotation,
mapped_states, src_mask]
prediction_outputs = [probs, context, alpha]
predict = theano.function(prediction_inputs, prediction_outputs)
generation_inputs = [prev_words, initial_state, context]
generation_outputs = next_state
generate = theano.function(generation_inputs, generation_outputs)
self.cost = cost
self.inputs = training_inputs
self.outputs = training_outputs
self.encode = encode
self.predict = predict
self.generate = generate
self.option = option
def __init__(self, **option):
# source and target embedding dim
sedim, tedim, xposhdim, yposhdim = option["embdim"]
# source, target and attention hidden dim
shdim, thdim, ahdim, xposnn, yposnn, word2pos, pos2word, pos2pos = option[
"hidden"]
# maxout hidden dim
maxdim = option["maxhid"]
# maxout part
maxpart = option["maxpart"]
# deepout hidden dim
deephid = option["deephid"]
svocab, tvocab, tagvocab = option["vocabulary"]
sw2id, sid2w = svocab
tw2id, tid2w = tvocab
stag2id, ttag2id = tagvocab
# source and target vocabulary size
svsize, tvsize = len(sid2w), len(tid2w)
stagsize, ttagsize = len(stag2id), len(ttag2id)
if "scope" not in option or option["scope"] is None:
option["scope"] = "rnnsearch"
if "initializer" not in option:
option["initializer"] = None
if "regularizer" not in option:
option["regularizer"] = None
if "keep_prob" not in option:
option["keep_prob"] = 1.0
dtype = theano.config.floatX
initializer = option["initializer"]
regularizer = option["regularizer"]
keep_prob = option["keep_prob"] or 1.0
scope = option["scope"]
decoder_scope = "decoder"
encoder = Encoder(sedim, shdim)
decoderType = eval("Decoder{}".format(option["decoder"]))
decoder = decoderType(tedim,
thdim,
ahdim,
2 * shdim + xposhdim,
dim_maxout=maxdim,
max_part=maxpart,
dim_readout=deephid,
n_y_vocab=tvsize,
n_y_tagvocab=ttagsize,
poshdim=yposhdim,
posnndim=yposnn,
word2pos=word2pos,
pos2word=pos2word,
pos2pos=pos2pos)
# training graph
with ops.variable_scope(scope,
initializer=initializer,
regularizer=regularizer,
dtype=dtype):
src_seq = T.imatrix("source_sequence")
src_mask = T.matrix("source_sequence_mask")
tgt_seq = T.imatrix("target_sequence")
tgt_mask = T.matrix("target_sequence_mask")
src_pos = T.imatrix("source_postag")
tgt_pos = T.imatrix("target_postag")
with ops.variable_scope("source_embedding"):
source_embedding = ops.get_variable("embedding",
[svsize, sedim])
source_bias = ops.get_variable("bias", [sedim])
with ops.variable_scope("target_embedding"):
target_embedding = ops.get_variable("embedding",
[tvsize, tedim])
target_bias = ops.get_variable("bias", [tedim])
with ops.variable_scope("srctag_embedding"):
srctag_embedding = ops.get_variable("embedding",
[stagsize, xposhdim])
srctag_bias = ops.get_variable("bias", [xposhdim])
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
source_inputs = source_inputs + source_bias
target_inputs = target_inputs + target_bias
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
with ops.variable_scope("srcpostagger"):
tempstates = nn.feedforward(annotation, [shdim * 2, xposnn],
True,
scope="staggerstates",
activation=T.nnet.relu)
scores = nn.linear(tempstates, [xposnn, stagsize],
True,
scope="staggerscores")
new_shape = [scores.shape[0] * scores.shape[1], -1]
scores = scores.reshape(new_shape)
srcposprobs = T.nnet.softmax(scores)
srctaggerstates = T.dot(srcposprobs,
srctag_embedding) + srctag_bias
srctaggerstates = srctaggerstates.reshape(
[annotation.shape[0], annotation.shape[1], -1])
idx = T.arange(src_pos.flatten().shape[0])
ce = -T.log(srcposprobs[idx, src_pos.flatten()])
ce = ce.reshape(src_pos.shape)
ce = T.sum(ce * src_mask, 0)
srcpos_cost = T.mean(ce)
tempposkeys = T.concatenate([srctaggerstates, tempstates], -1)
src_words_keys = map_key(annotation, 2 * shdim, ahdim,
"srcwordkeys")
src_pos_keys = map_key(tempposkeys, xposnn + xposhdim, word2pos,
"srcposkeys")
pos_words_keys = map_key(annotation, 2 * shdim, pos2word,
"pos2wordkeys")
pos_pos_keys = map_key(tempposkeys, xposnn + xposhdim, pos2pos,
"pos2poskeys")
annotation = T.concatenate([annotation, srctaggerstates], -1)
# compute initial state for decoder
# first state of backward encoder
final_state = T.concatenate([r_states[0], srctaggerstates[0]], -1)
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state,
[shdim + xposhdim, thdim],
True,
scope="initial",
activation=T.tanh)
_, _, transcost, _, tgtpos_cost = decoder.forward(
tgt_seq, target_inputs, tgt_mask, src_words_keys,
src_pos_keys, pos_words_keys, pos_pos_keys, src_mask,
annotation, initial_state, tgt_pos, keep_prob)
lambx = theano.shared(numpy.asarray(option["lambda"][0], dtype),
"lambdax")
lamby = theano.shared(numpy.asarray(option["lambda"][1], dtype),
"lambday")
totalcost = transcost + lambx * srcpos_cost + lamby * tgtpos_cost
training_inputs = [
src_seq, src_mask, tgt_seq, tgt_mask, src_pos, tgt_pos
]
training_outputs = [srcpos_cost, tgtpos_cost, transcost, totalcost]
# decoding graph
with ops.variable_scope(scope, reuse=True):
prev_words = T.ivector("prev_words")
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
source_inputs = source_inputs + source_bias
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
target_inputs = target_inputs + target_bias
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
with ops.variable_scope("srcpostagger"):
tempstates = nn.feedforward(annotation, [shdim * 2, xposnn],
True,
scope="staggerstates",
activation=T.nnet.relu)
scores = nn.linear(tempstates, [xposnn, stagsize],
True,
scope="staggerscores")
new_shape = [scores.shape[0] * scores.shape[1], -1]
scores = scores.reshape(new_shape)
srcposprobs = T.nnet.softmax(scores)
srctaggerstates = T.dot(srcposprobs,
srctag_embedding) + srctag_bias
srctaggerstates = srctaggerstates.reshape(
[annotation.shape[0], annotation.shape[1], -1])
tempposkeys = T.concatenate([srctaggerstates, tempstates], -1)
src_words_keys = map_key(annotation, 2 * shdim, ahdim,
"srcwordkeys")
src_pos_keys = map_key(tempposkeys, xposnn + xposhdim, word2pos,
"srcposkeys")
pos_words_keys = map_key(annotation, 2 * shdim, pos2word,
"pos2wordkeys")
pos_pos_keys = map_key(tempposkeys, xposnn + xposhdim, pos2pos,
"pos2poskeys")
annotation = T.concatenate([annotation, srctaggerstates], -1)
# decoder
final_state = T.concatenate([r_states[0], srctaggerstates[0]], -1)
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state,
[shdim + xposhdim, thdim],
True,
scope="initial",
activation=T.tanh)
prev_inputs = nn.embedding_lookup(target_embedding, prev_words)
prev_inputs = prev_inputs + target_bias
cond = T.neq(prev_words, 0)
# zeros out embedding if y is 0, which indicates <s>
prev_inputs = prev_inputs * cond[:, None]
with ops.variable_scope(decoder_scope):
mask = T.ones_like(prev_words, dtype=dtype)
next_state, context, next_pos, tgtposprob = decoder.step(
prev_inputs, mask, initial_state, src_words_keys,
src_pos_keys, pos_words_keys, pos_pos_keys, annotation,
src_mask)
if option["decoder"] == "GruSimple":
probs = decoder.prediction(prev_inputs, initial_state,
context)
elif option["decoder"] == "GruCond":
probs = decoder.prediction(prev_inputs, next_state,
context, next_pos)
# encoding
encoding_inputs = [src_seq, src_mask]
encoding_outputs = [
annotation, initial_state, src_words_keys, src_pos_keys,
pos_words_keys, pos_pos_keys, srcposprobs
]
encode = theano.function(encoding_inputs, encoding_outputs)
if option["decoder"] == "GruSimple":
prediction_inputs = [
prev_words, initial_state, annotation, mapped_keys, src_mask
]
prediction_outputs = [probs, context]
predict = theano.function(prediction_inputs, prediction_outputs)
generation_inputs = [prev_words, initial_state, context]
generation_outputs = next_state
generate = theano.function(generation_inputs, generation_outputs)
self.predict = predict
self.generate = generate
elif option["decoder"] == "GruCond":
prediction_inputs = [
prev_words, initial_state, annotation, src_words_keys,
src_pos_keys, pos_words_keys, pos_pos_keys, src_mask
]
prediction_outputs = [probs, next_state, tgtposprob]
predict = theano.function(prediction_inputs,
prediction_outputs,
on_unused_input='warn')
self.predict = predict
self.cost = totalcost
self.inputs = training_inputs
self.outputs = training_outputs
self.updates = []
self.encode = encode
self.option = option
def __init__(self, **option):
# source and target embedding dim
sedim, tedim = option["embdim"]
# source, target and attention hidden dim
shdim, thdim, ahdim = option["hidden"]
# maxout hidden dim
maxdim = option["maxhid"]
# maxout part
maxpart = option["maxpart"]
# deepout hidden dim
deephid = option["deephid"]
svocab, tvocab = option["vocabulary"]
sw2id, sid2w = svocab
tw2id, tid2w = tvocab
# source and target vocabulary size
svsize, tvsize = len(sid2w), len(tid2w)
if "scope" not in option or option["scope"] is None:
option["scope"] = "rnnsearch"
if "initializer" not in option:
option["initializer"] = None
if "regularizer" not in option:
option["regularizer"] = None
if "keep_prob" not in option:
option["keep_prob"] = 1.0
dtype = theano.config.floatX
initializer = option["initializer"]
regularizer = option["regularizer"]
keep_prob = option["keep_prob"] or 1.0
scope = option["scope"]
decoder_scope = "decoder2"
encoder = Encoder(sedim, shdim)
import decoder2
decoder = decoder2.DecoderGruCond(2,
option['method'],
tedim,
thdim,
ahdim,
2 * shdim + thdim,
dim_readout=deephid,
n_y_vocab=tvsize)
# training graph
with ops.variable_scope(scope,
initializer=initializer,
regularizer=regularizer,
dtype=dtype):
src_seq = T.imatrix("source_sequence")
src_mask = T.matrix("source_sequence_mask")
tgt_seq = T.imatrix("target_sequence")
tgt_mask = T.matrix("target_sequence_mask")
byseq = T.imatrix("backward_target_sequence")
with ops.variable_scope("source_embedding"):
source_embedding = ops.get_variable("embedding",
[svsize, sedim])
source_bias = ops.get_variable("bias", [sedim])
with ops.variable_scope("target_embedding"):
target_embedding = ops.get_variable("embedding",
[tvsize, tedim])
target_bias = ops.get_variable("bias", [tedim])
source_inputs = nn.embedding_lookup(source_embedding,
src_seq) + source_bias
target_inputs = nn.embedding_lookup(target_embedding,
tgt_seq) + target_bias
by_inputs = nn.embedding_lookup(target_embedding,
byseq) + target_bias
if keep_prob < 1.0:
source_inputs = nn.dropout(source_inputs, keep_prob=keep_prob)
target_inputs = nn.dropout(target_inputs, keep_prob=keep_prob)
by_inputs = nn.dropout(by_inputs, keep_prob=keep_prob)
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
annotation = nn.dropout(annotation, keep_prob=keep_prob)
import softdec
soft_decoder = softdec.SoftDecoder(option["eosid"],
option["softk"],
tedim,
thdim,
ahdim,
2 * shdim,
dim_readout=deephid,
n_y_vocab=tvsize)
with ops.variable_scope('soft_decoder'):
initial_state = nn.feedforward(states[-1], [shdim, thdim],
True,
scope='initial',
activation=T.tanh)
mapped_keys = map_key(annotation, 2 * shdim, ahdim)
soft_states, _, _, soft_mask = soft_decoder.infer(
mapped_keys, src_mask, annotation, initial_state,
target_embedding, target_bias, keep_prob)
with ops.variable_scope('soft_decoder', reuse=True):
_, _, soft_cost, _ = soft_decoder.forward(
byseq, by_inputs, tgt_mask, mapped_keys, src_mask,
annotation, initial_state, keep_prob)
# compute initial state for decoder
# first state of backward encoder
# initialize with only encoder state
final_state = r_states[0]
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True,
scope="initial",
activation=T.tanh)
# keys for query
with ops.variable_scope('map-key-src'):
mapped_keys_src = map_key(annotation, 2 * shdim, ahdim)
with ops.variable_scope('map-key-soft'):
mapped_keys_soft = map_key(soft_states, thdim, ahdim)
_, _, _, snt_cost = decoder.forward(
tgt_seq, target_inputs, tgt_mask,
[mapped_keys_src, mapped_keys_soft], [src_mask, soft_mask],
[annotation, soft_states], initial_state, keep_prob)
ce = snt_cost
true_cost = T.mean(ce)
lamb = theano.shared(numpy.asarray(option['lambda'], dtype),
'lambda')
cost = lamb * soft_cost + (1 - lamb) * true_cost
# import utils.ttensor
# print 'true_cost %d:' % len(utils.ttensor.find_inputs_and_params(true_cost)[0])
# for xxx in utils.ttensor.find_inputs_and_params(true_cost)[0]:
# print '\t', xxx
# print 'soft_cost %d:' % len(utils.ttensor.find_inputs_and_params(soft_cost)[0])
# for xxx in utils.ttensor.find_inputs_and_params(soft_cost)[0]:
# print '\t', xxx
# print 'tot_cost: %d' % len(utils.ttensor.find_inputs_and_params(cost)[0])
# for xxx in utils.ttensor.find_inputs_and_params(cost)[0]:
# print '\t', xxx
# print 'snt_cost: %d' % len(utils.ttensor.find_inputs_and_params(snt_cost)[0])
# for xxx in utils.ttensor.find_inputs_and_params(snt_cost)[0]:
# print '\t', xxx
training_inputs = [src_seq, src_mask, tgt_seq, tgt_mask, byseq]
training_outputs = [cost, soft_cost, true_cost]
# get_snt_cost = theano.function(training_inputs[:4], snt_cost)
get_snt_cost = None
# decoding graph
with ops.variable_scope(scope, reuse=True):
prev_words = T.ivector("prev_words")
# disable dropout
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
source_inputs = source_inputs + source_bias
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
target_inputs = target_inputs + target_bias
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
with ops.variable_scope('soft_decoder'):
initial_state = nn.feedforward(states[-1], [shdim, thdim],
True,
scope='initial',
activation=T.tanh)
mapped_keys = map_key(annotation, 2 * shdim, ahdim)
soft_states, soft_contexts, soft_probs, soft_mask = soft_decoder.infer(
mapped_keys, src_mask, annotation, initial_state,
target_embedding, target_bias, 1.0)
# decoder
final_state = r_states[0]
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True,
scope="initial",
activation=T.tanh)
# keys for query
with ops.variable_scope('map-key-src'):
mapped_keys_src = map_key(annotation, 2 * shdim, ahdim)
with ops.variable_scope('map-key-soft'):
mapped_keys_soft = map_key(soft_states, thdim, ahdim)
prev_inputs = nn.embedding_lookup(target_embedding, prev_words)
prev_inputs = prev_inputs + target_bias
cond = T.neq(prev_words, 0)
# zeros out embedding if y is 0, which indicates <s>
prev_inputs = prev_inputs * cond[:, None]
with ops.variable_scope(decoder_scope):
mask = T.ones_like(prev_words, dtype=dtype)
next_state, context = decoder.step(
prev_inputs, mask, initial_state, *[
mapped_keys_src, mapped_keys_soft, annotation,
soft_states, src_mask, soft_mask
])
probs = decoder.prediction(prev_inputs, next_state, context)
# encoding
encoding_inputs = [src_seq, src_mask]
encoding_outputs = [
initial_state, annotation, soft_states, mapped_keys_src,
mapped_keys_soft, soft_mask
]
encode = theano.function(encoding_inputs, encoding_outputs)
if option["decoder"] == "GruSimple":
raise ValueError()
prediction_inputs = [
prev_words, initial_state, annotation, mapped_keys, src_mask
]
prediction_outputs = [probs, context]
predict = theano.function(prediction_inputs, prediction_outputs)
generation_inputs = [prev_words, initial_state, context]
generation_outputs = next_state
generate = theano.function(generation_inputs, generation_outputs)
self.predict = predict
self.generate = generate
elif option["decoder"] == "GruCond":
prediction_inputs = [
prev_words, initial_state, annotation, mapped_keys_src,
src_mask, soft_states, mapped_keys_soft, soft_mask
]
prediction_outputs = [probs, next_state]
predict = theano.function(prediction_inputs, prediction_outputs)
self.predict = predict
self.cost = cost
self.inputs = training_inputs
self.outputs = training_outputs
self.updates = []
self.align = None
self.sample = None
self.encode = encode
self.get_snt_cost = get_snt_cost
self.option = option
def __init__(self, **option):
# source and target embedding dim
sedim, tedim = option["embdim"]
# source, target and attention hidden dim
shdim, thdim, ahdim, domaindim, feadim = option["hidden"]
# maxout hidden dim
maxdim = option["maxhid"]
# maxout part
maxpart = option["maxpart"]
# deepout hidden dim
deephid = option["deephid"]
svocab, tvocab = option["vocabulary"]
sw2id, sid2w = svocab
tw2id, tid2w = tvocab
# source and target vocabulary size
svsize, tvsize = len(sid2w), len(tid2w)
dnum = option['dnum']
if "scope" not in option or option["scope"] is None:
option["scope"] = "rnnsearch"
if "initializer" not in option:
option["initializer"] = None
if "regularizer" not in option:
option["regularizer"] = None
if "keep_prob" not in option:
option["keep_prob"] = 1.0
dtype = theano.config.floatX
initializer = option["initializer"]
regularizer = option["regularizer"]
keep_prob = option["keep_prob"] or 1.0
scope = option["scope"]
decoder_scope = "decoder"
encoder = Encoder(sedim, shdim)
decoderType = eval("Decoder{}".format(option["decoder"]))
decoder = decoderType(tedim,
thdim,
ahdim,
2 * shdim,
dnum=dnum,
dim_maxout=maxdim,
max_part=maxpart,
dim_readout=deephid,
dim_domain=domaindim,
feadim=feadim,
n_y_vocab=tvsize)
# training graph
with ops.variable_scope(scope,
initializer=initializer,
regularizer=regularizer,
dtype=dtype):
src_seq = T.imatrix("source_sequence")
src_mask = T.matrix("source_sequence_mask")
tgt_seq = T.imatrix("target_sequence")
tgt_mask = T.matrix("target_sequence_mask")
tag_seq = T.imatrix("domain_tag")
# nsrc_mask = T.set_subtensor(src_mask[T.cast(T.sum(src_mask, 0) - 1, 'int32'),
# T.arange(src_mask.shape[1])], 0.0)
with ops.variable_scope("source_embedding"):
source_embedding = ops.get_variable("embedding",
[svsize, sedim])
source_bias = ops.get_variable("bias", [sedim])
with ops.variable_scope("target_embedding") as tgtembscope:
target_embedding = ops.get_variable("embedding",
[tvsize, tedim])
# target_bias = ops.get_variable("bias", [tedim])
decoder.tiescope = tgtembscope
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
source_inputs = source_inputs + source_bias
if keep_prob < 1.0:
source_inputs = nn.dropout(source_inputs, keep_prob=keep_prob)
target_inputs = nn.dropout(target_inputs, keep_prob=keep_prob)
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
with ops.variable_scope("Specific"):
domain_alpha = domain_sensitive_attention(
annotation, src_mask, shdim * 2, domaindim)
# domain_alpha = attention(r_states[0], annotation, nsrc_mask,
# shdim,
# shdim * 2)
domain_context = T.sum(annotation * domain_alpha[:, :, None],
0)
dfeature = nn.feedforward(domain_context, [shdim * 2, feadim],
True,
activation=T.tanh,
scope="feature1")
dscores = nn.feedforward(dfeature, [feadim, dnum],
True,
activation=T.tanh,
scope="score")
# (batch, 2)
dprobs = T.nnet.softmax(dscores)
dpred_tag = T.argmax(dprobs, 1)
didx = T.arange(tag_seq.flatten().shape[0])
dce = -T.log(dprobs[didx, tag_seq.flatten()])
dcost = T.mean(dce)
share_alpha = domain_sensitive_attention(annotation, src_mask,
shdim * 2, domaindim)
# share_alpha = attention(r_states[0], annotation, nsrc_mask,
# shdim,
# shdim * 2)
share_context = T.sum(annotation * share_alpha[:, :, None], 0)
sfeature = nn.feedforward(share_context, [shdim * 2, feadim],
True,
activation=T.tanh,
scope="feature1")
with ops.variable_scope("Shared"):
sscores = nn.feedforward(sfeature, [feadim, dnum],
True,
activation=T.tanh,
scope="score")
# (batch, 2)
sprobs = T.nnet.softmax(sscores)
spred_tag = T.argmax(sprobs, 1)
sidx = T.arange(tag_seq.flatten().shape[0])
sce = -T.log(sprobs[sidx, tag_seq.flatten()])
scost = T.mean(sce)
adv_sce = -sprobs[sidx, tag_seq.flatten()] * T.log(
sprobs[sidx, tag_seq.flatten()])
adv_scost = T.mean(adv_sce)
domain_gate = nn.feedforward([dfeature, annotation],
[[feadim, shdim * 2], shdim * 2],
True,
scope="domain_gate")
domain_annotation = annotation * domain_gate
domain_annotation = nn.dropout(domain_annotation,
keep_prob=keep_prob)
share_gate = nn.feedforward([sfeature, annotation],
[[feadim, shdim * 2], shdim * 2],
True,
scope="share_gate")
annotation = annotation * share_gate
annotation = nn.dropout(annotation, keep_prob=keep_prob)
# compute initial state for decoder
# first state of backward encoder
# batch * shdim
final_state = T.concatenate([
annotation[0, :, annotation.shape[-1] / 2:],
domain_annotation[0, :, annotation.shape[-1] / 2:]
], -1)
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim * 2, thdim],
True,
scope="initial",
activation=T.tanh)
# keys for query
mapped_keys = map_key(annotation, 2 * shdim, ahdim, "semantic")
mapped_domain_keys = map_key(domain_annotation, 2 * shdim,
ahdim, "domain")
_, _, cost, tgtdcost, tpred_tag, _ = decoder.forward(
tgt_seq, target_inputs, tgt_mask, mapped_keys, src_mask,
annotation, initial_state, mapped_domain_keys,
domain_annotation, tag_seq, keep_prob)
lamb = theano.shared(numpy.asarray(option["lambda"], dtype), "lambda")
# cwscost *= lamb
final_cost = cost + dcost + tgtdcost - lamb * adv_scost
tag_inputs = [src_seq, src_mask]
tag_outputs = [dpred_tag, spred_tag]
tag_predict = theano.function(tag_inputs, tag_outputs)
self.tag_predict = tag_predict
tgt_tag_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
tgt_tag_outputs = [tpred_tag]
tgt_tag_predict = theano.function(tgt_tag_inputs, tgt_tag_outputs)
self.tgt_tag_predict = tgt_tag_predict
training_inputs = [src_seq, src_mask, tgt_seq, tgt_mask, tag_seq]
training_outputs = [cost, dcost, adv_scost, tgtdcost]
self.cost_cla = scost
self.inputs_cla = [src_seq, src_mask, tag_seq]
self.outputs_cla = [scost]
# decoding graph
with ops.variable_scope(scope, reuse=True):
prev_words = T.ivector("prev_words")
# disable dropout
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
source_inputs = source_inputs + source_bias
states, r_states = encoder.forward(source_inputs, src_mask)
annotation = T.concatenate([states, r_states], 2)
with ops.variable_scope("Specific"):
domain_alpha = domain_sensitive_attention(
annotation, src_mask, shdim * 2, domaindim)
# domain_alpha = attention(r_states[0], annotation, nsrc_mask,
# shdim,
# shdim * 2)
domain_context = T.sum(annotation * domain_alpha[:, :, None],
0)
dfeature = nn.feedforward(domain_context, [shdim * 2, feadim],
True,
activation=T.tanh,
scope="feature1")
share_alpha = domain_sensitive_attention(annotation, src_mask,
shdim * 2, domaindim)
# share_alpha = attention(r_states[0], annotation, nsrc_mask,
# shdim,
# shdim * 2)
share_context = T.sum(annotation * share_alpha[:, :, None], 0)
sfeature = nn.feedforward(share_context, [shdim * 2, feadim],
True,
activation=T.tanh,
scope="feature1")
domain_gate = nn.feedforward([dfeature, annotation],
[[feadim, shdim * 2], shdim * 2],
True,
scope="domain_gate")
domain_annotation = annotation * domain_gate
share_gate = nn.feedforward([sfeature, annotation],
[[feadim, shdim * 2], shdim * 2],
True,
scope="share_gate")
annotation = annotation * share_gate
# decoder
final_state = T.concatenate([
annotation[0, :, annotation.shape[-1] / 2:],
domain_annotation[0, :, annotation.shape[-1] / 2:]
], -1)
with ops.variable_scope(decoder_scope):
initial_state = nn.feedforward(final_state, [shdim * 2, thdim],
True,
scope="initial",
activation=T.tanh)
mapped_keys = map_key(annotation, 2 * shdim, ahdim, "semantic")
mapped_domain_keys = map_key(domain_annotation, 2 * shdim,
ahdim, "domain")
prev_inputs = nn.embedding_lookup(target_embedding, prev_words)
# prev_inputs = prev_inputs + target_bias
cond = T.neq(prev_words, 0)
# zeros out embedding if y is 0, which indicates <s>
prev_inputs = prev_inputs * cond[:, None]
with ops.variable_scope(decoder_scope):
mask = T.ones_like(prev_words, dtype=dtype)
next_state, context = decoder.step(prev_inputs, mask,
initial_state, mapped_keys,
annotation, src_mask,
mapped_domain_keys,
domain_annotation)
if option["decoder"] == "GruSimple":
probs = decoder.prediction(prev_inputs, initial_state,
context)
elif option["decoder"] == "GruCond":
probs = decoder.prediction(prev_inputs, next_state,
context)
# encoding
encoding_inputs = [src_seq, src_mask]
encoding_outputs = [
annotation, initial_state, mapped_keys, mapped_domain_keys,
domain_annotation
]
encode = theano.function(encoding_inputs, encoding_outputs)
if option["decoder"] == "GruSimple":
prediction_inputs = [
prev_words, initial_state, annotation, mapped_keys, src_mask
]
prediction_outputs = [probs, context]
predict = theano.function(prediction_inputs, prediction_outputs)
generation_inputs = [prev_words, initial_state, context]
generation_outputs = next_state
generate = theano.function(generation_inputs, generation_outputs)
self.predict = predict
self.generate = generate
elif option["decoder"] == "GruCond":
prediction_inputs = [
prev_words, initial_state, annotation, mapped_keys, src_mask,
mapped_domain_keys, domain_annotation
]
prediction_outputs = [probs, next_state]
predict = theano.function(prediction_inputs, prediction_outputs)
self.predict = predict
self.cost = final_cost
self.inputs = training_inputs
self.outputs = training_outputs
self.updates = []
# self.align = align
# self.sample = sample
self.encode = encode
# self.get_snt_cost = get_snt_cost
self.option = option
def __init__(self, **option):
# source and target embedding dim
sedim, tedim = option["embdim"]
# source, target and attention hidden dim
shdim, thdim, ahdim = option["hidden"]
# maxout hidden dim
maxdim = option["maxhid"]
# maxout part
maxpart = option["maxpart"]
# deepout hidden dim
deephid = option["deephid"]
svocab, tvocab = option["vocabulary"]
sw2id, sid2w = svocab
tw2id, tid2w = tvocab
# source and target vocabulary size
svsize, tvsize = len(sid2w), len(tid2w)
if "scope" not in option or option["scope"] is None:
option["scope"] = "rnnsearch"
if "initializer" not in option:
option["initializer"] = None
if "regularizer" not in option:
option["regularizer"] = None
if "criterion" not in option:
option["criterion"] = "mle"
if "keep_prob" not in option:
option["keep_prob"] = 1.0
dtype = theano.config.floatX
scope = option["scope"]
criterion = option["criterion"]
initializer = option["initializer"]
regularizer = option["regularizer"]
keep_prob = option["keep_prob"] or 1.0
# MRT mode do not use dropout
if criterion == "mrt":
keep_prob = 1.0
def prediction(prev_inputs, prev_state, context, keep_prob=1.0):
features = [prev_state, prev_inputs, context]
maxhid = nn.maxout(features, [[thdim, tedim, 2 * shdim], maxdim],
maxpart, True)
readout = nn.linear(maxhid, [maxdim, deephid],
False,
scope="deepout")
if keep_prob < 1.0:
readout = nn.dropout(readout, keep_prob=keep_prob)
logits = nn.linear(readout, [deephid, tvsize],
True,
scope="logits")
if logits.ndim == 3:
new_shape = [logits.shape[0] * logits.shape[1], -1]
logits = logits.reshape(new_shape)
probs = theano.tensor.nnet.softmax(logits)
return probs
# training graph
with ops.variable_scope(scope,
initializer=initializer,
regularizer=regularizer,
dtype=dtype):
src_seq = theano.tensor.imatrix("soruce_sequence")
src_mask = theano.tensor.matrix("soruce_sequence_mask")
tgt_seq = theano.tensor.imatrix("target_sequence")
tgt_mask = theano.tensor.matrix("target_sequence_mask")
if criterion == "mrt":
loss = theano.tensor.vector("loss_score")
sharp = theano.tensor.scalar("sharpness")
with ops.variable_scope("source_embedding"):
source_embedding = ops.get_variable("embedding",
[svsize, sedim])
source_bias = ops.get_variable("bias", [sedim])
with ops.variable_scope("target_embedding"):
target_embedding = ops.get_variable("embedding",
[tvsize, tedim])
target_bias = ops.get_variable("bias", [tedim])
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
source_inputs = source_inputs + source_bias
target_inputs = target_inputs + target_bias
if keep_prob < 1.0:
source_inputs = nn.dropout(source_inputs, keep_prob=keep_prob)
target_inputs = nn.dropout(target_inputs, keep_prob=keep_prob)
cell = nn.rnn_cell.gru_cell([sedim, shdim])
outputs = encoder(cell, source_inputs, src_mask)
annotation = theano.tensor.concatenate(outputs, 2)
annotation = nn.dropout(annotation, keep_prob=keep_prob)
# compute initial state for decoder
# first state of backward encoder
final_state = outputs[1][0]
with ops.variable_scope("decoder"):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True,
scope="initial",
activation=theano.tensor.tanh)
# run decoder
cell = nn.rnn_cell.gru_cell([[tedim, 2 * shdim], thdim])
if criterion == "mrt":
# In MRT training, shape of src_seq and src_mask are assumed
# to have [len, 1]
batch = tgt_seq.shape[1]
with ops.variable_scope("decoder"):
mapped_states = attention(None, annotation, None, None,
[thdim, 2 * shdim, ahdim])
b_src_mask = theano.tensor.repeat(src_mask, batch, 1)
b_annotation = theano.tensor.repeat(annotation, batch, 1)
b_mapped_states = theano.tensor.repeat(mapped_states, batch, 1)
b_initial_state = theano.tensor.repeat(initial_state, batch, 0)
decoder_outputs = decoder(cell, target_inputs, tgt_mask,
b_initial_state, b_annotation,
b_src_mask, ahdim, b_mapped_states)
else:
decoder_outputs = decoder(cell, target_inputs, tgt_mask,
initial_state, annotation, src_mask,
ahdim)
all_output, all_context = decoder_outputs
shift_inputs = theano.tensor.zeros_like(target_inputs)
shift_inputs = theano.tensor.set_subtensor(shift_inputs[1:],
target_inputs[:-1])
if criterion == "mrt":
init_state = b_initial_state[None, :, :]
else:
init_state = initial_state[None, :, :]
all_states = theano.tensor.concatenate([init_state, all_output], 0)
prev_states = all_states[:-1]
with ops.variable_scope("decoder"):
probs = prediction(shift_inputs,
prev_states,
all_context,
keep_prob=keep_prob)
# compute cost
idx = theano.tensor.arange(tgt_seq.flatten().shape[0])
ce = -theano.tensor.log(probs[idx, tgt_seq.flatten()])
ce = ce.reshape(tgt_seq.shape)
ce = theano.tensor.sum(ce * tgt_mask, 0)
if criterion == "mle":
cost = theano.tensor.mean(ce)
else:
# ce is positive here
logp = -ce
score = sharp * logp
# safe softmax
score = score - theano.tensor.max(score)
score = theano.tensor.exp(score)
qprob = score / theano.tensor.sum(score)
risk = theano.tensor.sum(qprob * loss)
cost = risk
if criterion == "mle":
training_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
else:
training_inputs = [
src_seq, src_mask, tgt_seq, tgt_mask, loss, sharp
]
training_outputs = [cost]
# decoding graph
with ops.variable_scope(scope, reuse=True):
prev_words = theano.tensor.ivector("prev_words")
# disable dropout
source_inputs = nn.embedding_lookup(source_embedding, src_seq)
source_inputs = source_inputs + source_bias
target_inputs = nn.embedding_lookup(target_embedding, tgt_seq)
target_inputs = target_inputs + target_bias
cell = nn.rnn_cell.gru_cell([sedim, shdim])
outputs = encoder(cell, source_inputs, src_mask)
annotation = theano.tensor.concatenate(outputs, 2)
# decoder
final_state = outputs[1][0]
with ops.variable_scope("decoder"):
initial_state = nn.feedforward(final_state, [shdim, thdim],
True,
scope="initial",
activation=theano.tensor.tanh)
inputs = nn.embedding_lookup(target_embedding, prev_words)
inputs = inputs + target_bias
cond = theano.tensor.neq(prev_words, 0)
# zeros out embedding if y is 0
inputs = inputs * cond[:, None]
cell = nn.rnn_cell.gru_cell([[tedim, 2 * shdim], thdim])
# encode -> prediction -> generation
# prediction: prev_word + prev_state => context, next_word
# generation: curr_word + context + prev_state => next_state
# here, initial_state is merely a placeholder
with ops.variable_scope("decoder"):
# used in encoding
mapped_states = attention(None, annotation, None, None,
[thdim, 2 * shdim, ahdim])
# used in prediction
alpha = attention(initial_state, None, mapped_states, src_mask,
[thdim, 2 * shdim, ahdim])
context = theano.tensor.sum(alpha[:, :, None] * annotation, 0)
probs = prediction(inputs, initial_state, context)
# used in generation
output, next_state = cell([inputs, context], initial_state)
# encoding
encoding_inputs = [src_seq, src_mask]
encoding_outputs = [annotation, initial_state, mapped_states]
encode = theano.function(encoding_inputs, encoding_outputs)
prediction_inputs = [
prev_words, initial_state, annotation, mapped_states, src_mask
]
prediction_outputs = [probs, context, alpha]
predict = theano.function(prediction_inputs, prediction_outputs)
generation_inputs = [prev_words, initial_state, context]
generation_outputs = next_state
generate = theano.function(generation_inputs, generation_outputs)
# sampling graph, this feature is optional
with ops.variable_scope(scope, reuse=True):
max_len = theano.tensor.iscalar()
def sampling_loop(inputs, state, attn_states, attn_mask, m_states):
alpha = attention(state, None, m_states, attn_mask,
[thdim, 2 * shdim, ahdim])
context = theano.tensor.sum(alpha[:, :, None] * attn_states, 0)
probs = prediction(inputs, state, context)
next_words = ops.random.multinomial(probs).argmax(axis=1)
new_inputs = nn.embedding_lookup(target_embedding, next_words)
new_inputs = new_inputs + target_bias
output, next_state = cell([new_inputs, context], state)
return [next_words, new_inputs, next_state]
with ops.variable_scope("decoder"):
batch = src_seq.shape[1]
initial_inputs = theano.tensor.zeros([batch, tedim],
dtype=dtype)
outputs_info = [None, initial_inputs, initial_state]
nonseq = [annotation, src_mask, mapped_states]
outputs, updates = theano.scan(sampling_loop, [],
outputs_info,
nonseq,
n_steps=max_len)
sampled_words = outputs[0]
sampling_inputs = [src_seq, src_mask, max_len]
sampling_outputs = sampled_words
sample = theano.function(sampling_inputs,
sampling_outputs,
updates=updates)
# attention graph, this feature is optional
with ops.variable_scope(scope, reuse=True):
def attention_loop(inputs, mask, state, attn_states, attn_mask,
m_states):
mask = mask[:, None]
alpha = attention(state, None, m_states, attn_mask,
[thdim, 2 * shdim, ahdim])
context = theano.tensor.sum(alpha[:, :, None] * attn_states, 0)
output, next_state = cell([inputs, context], state)
next_state = (1.0 - mask) * state + mask * next_state
return [alpha, next_state]
with ops.variable_scope("decoder"):
seq = [target_inputs, tgt_mask]
outputs_info = [None, initial_state]
nonseq = [annotation, src_mask, mapped_states]
(alpha, state), updaptes = theano.scan(attention_loop, seq,
outputs_info, nonseq)
attention_score = alpha
alignment_inputs = [src_seq, src_mask, tgt_seq, tgt_mask]
alignment_outputs = attention_score
align = theano.function(alignment_inputs, alignment_outputs)
self.cost = cost
self.inputs = training_inputs
self.outputs = training_outputs
self.updates = []
self.align = align
self.sample = sample
self.encode = encode
self.predict = predict
self.generate = generate
self.option = option
